Nonparametric estimation of concave production technologies by entropic methods

An econometric methodology is developed for nonparametric estimation of concave production technologies. The methodology, bases on the priciple of maximum likelihood, uses entropic distance and concvex programming techniques to estimate production functions.convex programming, production functions, entropy

Buying from the Babbling Retailer? The Impact of Availability Information on Customer Behavior

Provision of real-time information by a firm to its customers has become prevalent in recent years in both the service and retail sectors. In this paper, we study a retail operations model where customers are strategic in both their actions and in the way they interpret information, whereas the retailer is strategic in the way it provides information. This paper focuses on the ability (or the lack thereof) to communicate unverifiable information and influence customers' actions. We develop a game-theoretic framework to study this type of communication and discuss the equilibrium language emerging between the retailer and its customers. We show that for a single retailer and homogeneous customer population setting, the equilibrium language that emerges carries no information. In this sense, a single retailer providing information on its own cannot create any credibility with the customers. We study how the results are impacted due to the heterogeneity of the customers. We provide conditions under which the firm may be able to influence the customer behavior. In particular, we show that the customers' willingness to pay and willingness to wait cannot be ranked in an opposite manner. However, even when the firm can influence each customer class separately, the effective demand is not impacted. This paper was accepted by Yossi Aviv, operations management.inventory, cheap talk, revenue management

Understanding customer retrials in call centers: preferences for service quality and service speed

Problem definition: Customers are likely to initiate retrial calls when their previous contact with a call center fails to deliver a satisfactory resolution. According to industry reports, retrials are listed as a top annoying issue for customers and hurt call centers’ profits. Though recognizing this problem, call centers find it challenging to reduce retrials without overshooting their operating expenses. Our research aims to empirically understand the mechanism of customer retrials and then provide economically feasible solutions to reduce retrials. Academic/practical relevance: Little empirical research has been done to understand customers’ strategic retrials, and theoretical research studies retrials by assuming the degree to which pickup speed and service quality impact retrials. Our research empirically investigates the mechanism of customer retrials by studying whether speed and quality truly matter and, if so, how strong the impact is from each of them and whether the impacts are different across various customer segments. The quantified mechanism can then guide service providers to reduce retrials cost-effectively. Methodology: We use a random-coefficient dynamic structural model to characterize customer decisions in pursuing a satisfactory resolution and estimate the parameters from call-by-call records of a uniquely designed call center. Our model tracks customer decisions in the online waiting stage, in which customers are waiting for an agent but weighing whether to abandon, and in the off-line waiting stage, in which customers are not directly connected but are actively debating whether to retry. Utilizing the hybrid system that sequentially places customers into queues for three distinct quality service groups, we disentangle the effects of pickup speed and service quality on customers’ abandonment and retrial decisions. Results: Our estimations confirm that high service quality and quick pickup speed reduce retrials. Moreover, we discover that private customers are more sensitive to quality but less sensitive to speed compared with business customers. We suggest two service designs to reduce retrials cost-effectively by tailoring services to customer preferences. One reallocates the service groups for different customer segments without expanding the system, and the other adjusts the staffing ratios by hiring low-cost, ordinary-quality agents. Under the two tailoring designs, business customer surplus increases by up to 14.4% and private customer surplus by up to 14.9%. Managerial implications: First, our research highlights the importance of recognizing customers’ off-line decisions, which are impacted by online service offerings and, in turn, affect future online service operations. Neglecting customer retrials leads to suboptimal service designs. Second, by understanding the mechanism of customer retrials empirically, our research guides call centers to reduce retrials cost-effectively with speed–quality balance. Third, our research develops a practical analysis framework for service providers to quantify customer preferences and design tailoring services

Global dual sourcing: Tailored base-surge allocation to near and offshore production

When designing a sourcing strategy in practice, a key task is to determine the average order rates placed toeach source because that affects cost and supplier management. We consider a firm that has access to a responsive nearshore source (e.g., Mexico) and a low-cost offshore source (e.g., China). The firm must determine an inventory sourcing policy to satisfy random demand over time. Unfortunately, the optimal policy is too complex to allow a direct answer to our key question. Therefore, we analyze a tailored base-surge (TBS) sourcing policy that is simple, used in practice, and captures the classic trade-off between cost and responsiveness. The TBS policy combines push and pull controls by replenishing at a constant rate from the offshore source and producing at the nearshore plant only when inventory is below a target. The constant base allocation allows the offshore facility to focus on cost efficiency, whereas the nearshore facility’s quick response capability is utilized only dynamically to guarantee high service. The research goals are to (i) determine the allocation of random demand into base and surge capacity, (ii) estimate corresponding working capital requirements, and (iii) identify and value the key drivers of dual sourcing. We present performance bounds on the optimal cost and prove that economic optimization brings the system into heavy traffic. We analyze the sourcing policy that is asymptotically optimal for high-volume systems and present a simple “square-root” formula that is insightful to answer our questions and sufficiently accurate for practice, as is demonstrated with a validation study